Merge branch 'main' into feature/distributed_log

Author: zoeyuchao

.github/workflows/embodied-e2e-tests.yml

@@ -78,6 +78,35 @@ jobs:
           rm -rf .venv
           uv cache prune
 
+  embodied-cnn-realworld-xsquare-turtle2-dummy-sac-test:
+    runs-on: embodied
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v5
+
+      - name: Create embodied environment
+        run: |
+          unset UV_DEFAULT_INDEX
+          export UV_PATH=/workspace/dataset/.uv
+          export UV_LINK_MODE=symlink
+          export UV_CACHE_DIR=/workspace/dataset/.uv_cache
+          export UV_PYTHON_INSTALL_DIR=/workspace/dataset/.uv_python
+          export LIBERO_PATH=/workspace/dataset/LIBERO
+          bash requirements/install.sh embodied --model openvla --env maniskill_libero
+
+      - name: Xsquare-turtle2 SAC dummy test
+        timeout-minutes: 20
+        run: |
+          unset PYTHONPATH
+          export REPO_PATH=$(pwd)
+          source .venv/bin/activate
+          bash tests/e2e_tests/embodied/run.sh realworld_xsquare_turtle2_dummy_sac_cnn
+
+      - name: Clean up
+        run: |
+          rm -rf .venv
+          uv cache prune
+
   embodied-mlp-frankasim-test:
     runs-on: embodied
     steps:

.github/workflows/install.yml

@@ -90,6 +90,14 @@ jobs:
           pip install uv
           bash requirements/install.sh embodied --model dexbotic --env maniskill_libero
           rm -rf .venv
+      
+      - name: Install xsquare_turtle2
+        run: |
+          pip install uv
+          uv cache prune --ci
+          bash requirements/install.sh embodied --env xsquare_turtle2
+          rm -rf .venv
+
 
       - name: Install behavior-openvla-oft
         run: |

docs/source-en/rst_source/examples/embodied/franka.rst

@@ -283,7 +283,7 @@ Prerequisites
 
 **Get the Target Pose for the Task**
 
-To acquire the target pose for the peg-insertion task, you can use the `toolkits.realworld_check.test_controller` script.
+To acquire the target pose for the peg-insertion task, you can use the `toolkits.realworld_check.test_franka_controller` script.
 
 First, you need to activate your Franka robot's programming mode, and manually move the robot to the desired target pose.
 
@@ -297,7 +297,7 @@ Next, run the script:
 
 .. code-block:: bash
 
-   python -m toolkits.realworld_check.test_controller
+   python -m toolkits.realworld_check.test_franka_controller
 
 The script will prompt you to input command, you can enter `getpos_euler` to get the current end-effector pose in Euler angles.
 
@@ -419,11 +419,11 @@ First, test the camera connection by running on the controller node:
 
 .. code-block:: bash
 
-   python -m toolkits.realworld_check.test_camera
+   python -m toolkits.realworld_check.test_franka_camera
 
-Next, test the basic cluster setup by running a dummy setup. Refer to ``examples/embodiment/config/real_world_dummy_sac_cnn.yaml`` and add `env.eval.override_cfg`.
+Next, test the basic cluster setup by running a dummy setup. Refer to ``examples/embodiment/config/realworld_dummy_franka_sac_cnn.yaml`` and add `env.eval.override_cfg`.
 You can set the `is_dummy` field to `True` in both `env.train.override_cfg` and `env.eval.override_cfg` sections in the configuration file to enable the dummy setup.
-And fill the camera serial numbers obtained from ``running toolkits.realworld_check.test_camera.py`` into the field `camera_serials` under both `env.train.override_cfg` and `env.eval.override_cfg`.
+And fill the camera serial numbers obtained from ``running toolkits.realworld_check.test_franka_camera.py`` into the field `camera_serials` under both `env.train.override_cfg` and `env.eval.override_cfg`.
 
 Then, run the test script on the head node:
 

docs/source-en/rst_source/examples/embodied/index.rst

@@ -247,6 +247,17 @@ as well as reinforcement learning training examples on real robots.
         Steer a frozen Pi0 diffusion policy with lightweight SAC in noise space
       </p>
     </div>
+
+    <div style="flex: 1 1 30%; max-width: 300px; text-align: center;">
+      <img src="https://github.com/RLinf/misc/raw/main/pic/xsquare_turtle2_arm_small.jpg"
+          style="width: 100%; height: 200px; object-fit: cover; border-radius: 8px; box-shadow: 0 2px 6px rgba(0,0,0,0.15);" />
+      <p style="margin-top: 8px; font-size: 14px; line-height: 1.4;">
+       <a href="xsquare_turtle2.html" style="text-decoration: underline; color: blue;">
+          <b>Real-World RL with XSquare Turtle2</b>
+        </a><br>
+        SAC + CNN policy on the XSquare Turtle2 dual-arm robot
+      </p>
+    </div>
   </div>
 
 
@@ -272,6 +283,7 @@ as well as reinforcement learning training examples on real robots.
    mlp
    dexbotic
    franka
+   xsquare_turtle2
    sft_openpi
    sft_vlm
    co_training